Shoreline change is a process that occurs due to the impact of natural factors and human activities. Geographically, the coastal area of North Central Timor Regency (NCT) is in the northern part of the island of Timor, East Nusa Tenggara Province (ENT). Physically, the area is affected by the oceanographic dynamics of the Sawu Sea waters and aquaculture activities, which impact the damage to coastal ecosystems. This study aims to analyze shoreline change in the northern coastal area of NCT Regency. The data used are Landsat 8 images from 2015-20221 to describe current conditions. Meanwhile, Landsat 5 imagery data from 1990 - 2000 was used to describe the initial conditions. The satellite imagery is analyzed to map shoreline changes that experience accretion or abrasion. The results show that the shoreline of the study area has experienced changes in accretion and abrasion. Based on the area of change in the northern coastal area of NCT Regency, the dominant accretion area was 1108.07 m2 with a rate of change of 20.19 m.year-1, as long as 1021 meters, while the abrasion was 845.43 m2 at a rate of 12.65 m.year-1 as long as 36520 meters. The average shoreline change distance in accretion conditions was 11.3 meters, while the abrasion was 7.93 meters. The shoreline shift due to the highest abrasion in Ponu was -16.08, while accretion in North Oepuah was 35.63 meters. The results of this research will contribute to planning the management of the coastal area of NCT Regency.

This research aimed to quantify the effects of biochar derived from corn straw on soil thermal conductivity, capacity, and diffusivity. Firstly, the amount of biochar application (w/w) added to light sierozem soil was 0% to 5%, and the mixtures were packed into soil columns at a consistent bulk density (1.20 g.cm-3). Secondly, soil columns with a consistent biochar addition rate (5%) were packed to different bulk densities of 1.30, 1.25, 1.20, 1.15, and 1.10 g.cm-3. Soil thermal characteristics were measured under the control of soil moisture content from 0% to 40%. Under consistent bulk-density conditions, biochar could significantly reduce soil thermal conductivity and diffusivity. Still, there wasn’t a significant influence on soil heat capacity in most soil moisture content levels. With the decrease of soil bulk density, soil thermal conductivity, capacity, and diffusion coefficient reduced significantly. As soil water content increased, all the indexes of thermal properties largely improved, and the effects were much more significant than those of biochar amendment and bulk density change on soil thermal performances. This research could supply an implication to evaluate the influence of biochar amendment on soil thermal performances.

Anaerobic digestion produces biogas which usually contains 60-70% of methane (CH4), 30-40% of carbon-di-oxide (CO2), and 10-2,000 ppm hydrogen sulfide (H2S). The concentration of H2S depends upon the type of substrate. H2S tends to corrode pipes and machines carrying them. The high concentrations of H2S present in biogas may adversely affect electricity generation. Hence, the removal of H2S and enrichment of biogas with CH4 is an essential step towards higher energy production. In the present study, the biological method of removing H2S using Thiobacillus sp. was demonstrated for a one cu.m anaerobic co-digestion (ACD) unit running on an organic fraction of municipal solid waste (OFMSW) and septage sludge. Initial lab scale studies were conducted by collecting the biogas generated from 1 cu.m digesters, and continuous experiments were optimized for the process parameters such as flow rate, the volume of medium with culture, time, the height of the column, column composition, etc. The raw biogas was purged in a liquid medium (LM) with a culture containing Thiobacillus sp. The studies with the LM containing Thiobacillus sp. culture showed a 68% removal of H2S in the first 8 min, and the saturation occurred at 75 min when the time-dependent experiment was studied. The smaller flow rate (0.48 L.min-1) and highest volume of culture (500 mL) showed better results than other parameters. The highest and average oxidation rates of sulfate were recorded as 39 and 40.3 ppm.sec-1, respectively, for 0.48 L.min-1 flow rate and 500 mL of the culture volume. In the column studies, a column containing cocopeat (CP) was studied for its efficiency in removing H2S. At a flow rate of 0.9 L.min-1, 25% adsorption was encountered and reached saturation at 90 min. The bed height of 9 inches with CP and plastic support (PS) showed a 20% H2S removal. The filling ratio of CP and PS (1:1) was the best ratio for proper gas passage with optimal time for adsorption/absorption. The kinetic, isotherm, and continuous models helped to understand the capacity of the adsorbent. Freundlich, Yoon-Nelson, and BDST model were best fit for the present study. A pilot scale setup for one cu.m biogas reactor showed an average of 50% removal of H2S for LM with culture, and an additional 20% removal was possible by the introduction of a column along with the liquid bed in series. An overall efficiency of 70-75% of H2S removal was achieved. No significant CH4 loss was encountered during the study.

As climate change continues to cause more frequent weather shocks such as droughts and floods and increasingly erratic rainfall, people in developing regions are threatened by crop failures and hunger. In this study, the researchers describe how climate change influences food security in Central Java, seen from the frequency of floods, rainfall, and rainy days. This study also added another variable, i.e., economic growth, reviewed through GRDP and the amount of rice production. Using the Common effect model, the study results revealed that rainy days and population were the variables significantly influencing food security in cities/regencies in Central Java Province. Meanwhile, two other variables, i.e., rainfall and GRDP, had no significant effect on food security in cities/regencies in Central Java Province.

Construction activities produce considerable environmental effects and have resulted in a growing demand to implement favorable environmental practices. In this sense, this research aimed to evaluate the effect of the level of compliance with environmental regulations in public civil works in the San Ignacio, Cajamarca, Peru district. Data were obtained through direct observation and structured interviews in 7 selected construction sites. The deductive and analytical method was used. As a result, the level of compliance was obtained. Work 4 had the highest rank, and works 2 and 5 had the lowest. Currently, all the works are in a similar range of compliance. The degree of association between the level of compliance with environmental regulations and the current state of the civil works indicates a probability of 0.0190, which shows that the low level of compliance with environmental regulations in the construction of public civil works in the district of San Ignacio generates a deterioration in the quality of the environment and increases the possibility of administrative sanctions.

The rapid depletion of the world’s fossil fuel reserves and global warming issues have promoted the search for sustainable alternative energy resources. In the present investigation, large-scale cultivation of naturally isolated freshwater microalgae Asterarcys quadricellulare strain was carried out using tertiary treated municipal wastewater as a growth medium in an open HRP pond for bioethanol production. A total of 12.091 kg of dry biomass was obtained at the end of the study. The lipid extracted carbohydrate rich spent microalgae biomass was converted to bioethanol by ethanolic fermentation. The biomass was first pre-treated with different concentrations of H2SO4 and HCL hydrolysis with different temperatures and reaction times. The biomass treated with a 2.0% concentration of H2SO4, showed maximum yields of glucose 308.38 mg.g-1 at 100°C with 180 min reaction time. The hydrolysates derived from the hydrolysis of microalgae biomass were used as a substrate for fermentation by using S. cerevisiae. The obtained bioethanol was analyzed using HPLC and the purity of ethanol was 90%.

Arsenic, aluminum, iron, lead, chromium, copper, zinc, manganese, and cadmium are some of the heavy metal pollutants in the air that cause severe impacts on the biotic and abiotic environment. This study intended to find the accumulation capacity of the heavy metals on the leaves of tree species such as Terminalia catappa, Syzygium cumini, Saraca asoca, Pongamia glabra, and Ficus religiosa and predict their accuracy by comparing different machine learning (ML) models. The samples were collected at six different locations (likely Vellagate, Cancer Institute, CSI hospital area, Moongilmandapam, Collectorate, and Pallavarmedu) and distributed in a manner within Kanchipuram town, Tamil Nadu, in February and March of 2018 and 2019, respectively. Six ML methods were selected, such as KStar (K*), Lazy IKB, Logistic Regression Algorithm (LR), LogitBoost Classifier (LB), Meta Randomizable Filtered Classifier (MRFC), and Random Tree (RT), for prediction and to compare the efficiency of their predictions. Out of six models, Logistic functions perform well in terms of TP rate when compared to other classifiers (93.21%-99.81% TPR– 0.93–0.99) and Logitboost attained a low TP rate that ranged from 0.76 to 0.82. This study indicates the feasibility of different ML methods in the prediction of species capabilities toward the accumulation of heavy metals.

The purpose of the paper is to estimate the environmental efficiency of the Vietnamese textile and garment industry and evaluate the impact of the factors on environmental efficiency. The study uses firm-level panel data from the Vietnam annual enterprise survey data for the 2012–2018 period in the Vietnam textile and garment industry to evaluate the environmental efficiency by using the Super-SBM DEA model with undesirable output and applies the Tobit regression model to measure the impact of the factors on the environmental efficiency. This study evaluates environmental efficiency and assesses the impact of some core factors, including the origin of imported machinery and equipment, the origin of imported materials, the management of industrial zones, and the presence of FDI firms, on environmental efficiency at the firm level. The results indicate that the average score for environmental efficiency is 0.233. Some factors, such as income per employee, machined goods imported from developed countries, industrial zones, firm improvement processes, and the presence of FDI, have a positive impact on a firm’s environmental efficiency, whereas materials made in Vietnam have a negative impact.

Households, restaurants, canteens, and hotel wastes constitute kitchen waste. Every day our growing cities generate more and more waste, which is overloading our municipal systems. The main aim of the present work was to prepare a microbial consortium that can effectively and rapidly bring about the degradation of kitchen wastes that can be used in agricultural soils. More than 100 different bacterial isolates were obtained from various kitchen waste dumping areas. The bacterial isolates were studied to produce enzymes like amylase, gelatinase, lipase, and protease on respective media plates. The best 20 isolates were subjected to enzyme quantification. The isolates showing maximum production for all four enzymes were selected for consortia preparation. The consortia of isolates were prepared by permutation combinations. Amongst all consortia prepared consortium No. 7 showed maximum enzymatic potential. The bacterial isolates in the best consortium (No. 7) were further characterized and identified as KW104 Serratia marcescens, KW37 Micrococcus luteus, KW128 Brevindimonas mediterranea, KW91 Bacillus tequilensis, and KW97 Exiguobacterium mexicanum. This consortium showed rapid degradation of waste as compared to others in 15 days duration of time showing good potential for compost formation when applied to plant growth.

Globally, vehicular pollution is one of the greatest concerns in urban areas. Several studies on air pollution have been conducted using deterministic, statistical, and soft computing methods. However, there has been little research on how soft-computing methods like Artificial Neural Networks (ANN) can help us comprehend vehicular pollution’s non-linear and highly complex dispersion. This study uses an ANN-based vehicular pollution model to investigate the effect of vehicular traffic on PM2.5 concentrations in built-up and open terrain-surrounding environments. Five distinct pollution models were developed for two locations in Delhi, considering PM2.5 pollutants, meteorological variables, traffic flow, and traffic composition into account. The results concluded that under open terrain conditions, the significance of the traffic variable in its association with PM2.5 is almost half the significance observed under built-up conditions. Also, in terms of PM2.5 reductions, the maximum reduction observed at Location-1 (built-up environment), and Location-2 (open terrain environment) is 1.85 and 2.44 times the percent reduction in traffic during peak hours, respectively. The study’s findings have significant ramifications for the current practices of ignoring the contribution of traffic and the built environment to pollution and adopting measures like an odd-even rule and high fuel and parking prices to combat pollution.